基于统计α算法的临床路径过程挖掘

摘要

针对临床路径事件日志中存在的重名活动和噪音数据,提出集成重名活动判别的过程挖掘算法:统计α算法.给出一套完整的重名活动的判别规则,用于识别过程挖掘中的重名活动并进行相应预处理,有效地提高了过程挖掘的准确性;提出基于经典α算法改进的统计α算法,用于消除事件日志中各种噪音的影响.该算法在临床路径数据量较大的情形下,保证了结果准确率和运算效率.统计α算法在三甲医院的临床数据上得到成功应用,与经典α算法和遗传算法相比,该算法在效率和准确性上更具优越性.%A process mining algorithm integrated with cognominal activities identification rules (called statistical α-algorithm) was proposed for dealing with the cognominal activities and noise in clinical pathway event logs.A set of cognominal activities identification rules was proposed for the pretreatment of process mining to identify and dispose th cognominal activities in the event logs,which improved the accuracy of the proposed method.The statistical α-algorithm was developed based on the classical α-algorithm to eliminate the influence of process noise in event logs.The proposed method showed high accuracy and efficiency when there were large amounts of clinical data.Statistical α-algorithm was successfully applied to the real-world clinical data from a hospital.The experimental results indicated that the algorithm was superior in efficiency and accuracy compared with the classical α-algorithm and the genetic algorithm.